Create README.md

README.md

@@ -0,0 +1,134 @@
+# Named Entity Recognition (NER) of Information Extraction
+
+## 📌 Overview
+This repository hosts the quantized version of the `bert-base-cased` model for Named Entity Recognition (NER) using the CoNLL-2003 dataset. The model is specifically designed to recognize entities related to **Person (PER), Organization (ORG), and Location (LOC)**. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.
+
+## 🏗 Model Details
+- **Model Architecture**: BERT Base Cased
+- **Task**: Named Entity Recognition (NER) of Information Extraction
+- **Dataset**: Hugging Face's CoNLL-2003
+- **Quantization**: BrainFloat16
+- **Fine-tuning Framework**: Hugging Face Transformers
+
+---
+## 🚀 Usage
+
+### Installation
+```bash
+pip install transformers torch
+```
+
+### Loading the Model
+```python
+from transformers import BertTokenizerFast, BertForTokenClassification
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_name = "AventIQ-AI/named-entity-recognition-for-information-extraction"
+model = BertForTokenClassification.from_pretrained(model_name).to(device)
+tokenizer = BertTokenizerFast.from_pretrained(model_name)
+```
+
+### Named Entity Recognition Inference
+```python
+label_list = ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC"]
+```
+### **🔹 Labeling Scheme (BIO Format)**
+ 
+- **B-XYZ (Beginning)**: Indicates the beginning of an entity of type XYZ (e.g., B-PER for the beginning of a person’s name).
+- **I-XYZ (Inside)**: Represents subsequent tokens inside an entity (e.g., I-PER for the second part of a person’s name).
+- **O (Outside)**: Denotes tokens that are not part of any named entity.
+
+```
+def predict_entities(text, model):
+
+    tokens = tokenizer(text, return_tensors="pt", truncation=True)
+    tokens = {key: val.to(device) for key, val in tokens.items()}  # Move to CUDA
+
+    with torch.no_grad():
+        outputs = model(**tokens)
+    
+    logits = outputs.logits  # Extract logits
+    predictions = torch.argmax(logits, dim=2)  # Get highest probability labels
+
+    tokens_list = tokenizer.convert_ids_to_tokens(tokens["input_ids"][0])
+    predicted_labels = [label_list[pred] for pred in predictions[0].cpu().numpy()]
+
+    final_tokens = []
+    final_labels = []
+    for token, label in zip(tokens_list, predicted_labels):
+        if token.startswith("##"):  
+            final_tokens[-1] += token[2:]  # Merge subword
+        else:
+            final_tokens.append(token)
+            final_labels.append(label)
+
+    for token, label in zip(final_tokens, final_labels):
+        if token not in ["[CLS]", "[SEP]"]:
+            print(f"{token}: {label}")
+
+# 🔍 Test Example
+sample_text = "Elon Musk is the CEO of Tesla, which is based in California."
+predict_entities(sample_text, model)
+```
+---
+## 📊 Evaluation Results for Quantized Model
+ 
+### **🔹 Overall Performance**
+ 
+- **Accuracy**: **97.10%** ✅
+- **Precision**: **89.52%**
+- **Recall**: **90.67%**
+- **F1 Score**: **90.09%**
+ 
+---
+ 
+### **🔹 Performance by Entity Type**
+ 
+| Entity Type | Precision | Recall | F1 Score | Number of Entities |
+|------------|-----------|--------|----------|--------------------|
+| **LOC** (Location) | **91.46%** | **92.07%** | **91.76%** | 3,000 |
+| **MISC** (Miscellaneous) | **71.25%** | **72.83%** | **72.03%** | 1,266 |
+| **ORG** (Organization) | **89.83%** | **93.02%** | **91.40%** | 3,524 |
+| **PER** (Person) | **95.16%** | **94.04%** | **94.60%** | 2,989 |
+
+ ---
+#### ⏳ **Inference Speed Metrics**
+- **Total Evaluation Time**: 15.89 sec
+- **Samples Processed per Second**: 217.26
+- **Steps per Second**: 27.18
+- **Epochs Completed**: 3
+ 
+---
+## Fine-Tuning Details
+### Dataset
+The Hugging Face's `CoNLL-2003` dataset was used, containing texts and their ner tags.
+
+## 📊 Training Details
+- **Number of epochs**: 3  
+- **Batch size**: 8  
+- **Evaluation strategy**: epoch
+- **Learning Rate**: 2e-5
+
+### ⚡ Quantization
+Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
+
+---
+## 📂 Repository Structure
+```
+.
+├── model/               # Contains the quantized model files
+├── tokenizer_config/    # Tokenizer configuration and vocabulary files
+├── model.safetensors/   # Quantized Model
+├── README.md            # Model documentation
+```
+
+---
+## ⚠️ Limitations
+- The model may not generalize well to domains outside the fine-tuning dataset.
+- Quantization may result in minor accuracy degradation compared to full-precision models.
+
+---
+## 🤝 Contributing
+Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.